you can’t make this stuff up.

Nice.

DeepSeek just dropped the single best end-to-end paper on large model training. It covers — Software (MLA, training in FP8, DeepEP, LogFMT) — Hardware (Multi-Rail Fat Tree, Ethernet RoCE switches) — Mix (IBGDA, 3FS filesystem) DeepSeek's engineering depth is insane. Must read.

I turned ChatGPT into a personal assistant, and now I only work for a few hours. Here are 10 ChatGPT prompts so powerful and useful that they feel illegal to use:

5 levels of Agentic AI systems, clearly explained (with visuals):

product manager using their 16” M4 Max Macbook Pro to open JIRA

YC on the key prompting techniques used by the best AI startups:

Lingo.dev Sometimes I register on platforms with “null” as my name to imagine the dev freaking out

An attempt to explain (current) ChatGPT versions. I still run into many, many people who don't know that: - o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3

if you’re not using Cursor like this, you’re using it wrong

Google opensources DeepSearch stack Get started with building Fullstack Agents using Gemini 2.5 and LangGraph 📝 Overview This project has a React frontend and a FastAPI backend built on LangGraph. The agent turns user input into search queries with Gemini, fetches web results

🔥 Introducing Firecrawl /search. Firecrawl just launched an insane feature to search and crawl in one shot. You heard that right! One API call to search the web and scrape any data you need for your AI agents. I took it for a spin in n8n:

Good post from Balaji on the "verification gap". You could see it as there being two modes in creation. Borrowing GAN terminology: 1) generation and 2) discrimination. e.g. painting - you make a brush stroke (1) and then you look for a while to see if you improved the

OpenAI, Google, and Anthropic released best guides on: - Prompt Engineering - Building effective Agents - AI in Business - 601 AI use cases and so much more... 9 best guides you can’t afford to miss:

On building your personalized deep research agents. I recently built this deep research agentic workflow with n8n and was very impressed by the results. Combining reasoning models + multi-agent workflows is like magic! A few things I learned along the way:

I think Andrej Karpathy just gave the best talk of Y Combinator AI Startup School. Software in the era of AI. Here are some of the main takeaways 👇

The smartest way to learn AI in 2025 is to follow the right people. Start here.

a quick note from Andrej Karpathy talk, which afterwards I was fully immersed in the talk I stopped writing down/taking notes: While it is interesting to think about LLMs in terms of "electricity" -> CAPEX to train an LLM (~= to build the grid) -> OPEX to serve intelligence over

Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again. LLMs are a new kind of computer, and you program them *in English*. Hence I think they are well deserving of a major version upgrade in terms of